AI Recommendations Engine

An AI Recommendations Engine for e-commerce goes beyond traditional product recommendations ("customers also bought"). It analyzes the full spectrum of business data—acquisition, retention, profitability, and customer behavior—to generate strategic recommendations for the business operator.

The engine works in four stages: Data Unification (consolidating data from e-commerce platforms, ad channels, email tools, and support systems), Pattern Detection (identifying anomalies, trends, and opportunities across all connected data), Impact Scoring (estimating the revenue impact of acting on each finding), and Prioritization (ranking recommendations by impact, urgency, and effort required).

Output recommendations span multiple domains: acquisition (which campaigns to scale or pause, where to shift budget), retention (which customer segments need intervention, what offers to deploy), and profitability (which products to promote, where margins are eroding). Each recommendation includes the specific action to take, the data behind the recommendation, and the expected impact—turning analytics from a passive reporting function into an active growth driver.